Postdoc in reinforcement learning for stochastic optimization at Vrije Universiteit Amsterdam
1AZ, Noord-Holland, Netherlands -
Full Time


Start Date

Immediate

Expiry Date

02 Aug, 25

Salary

3.378

Posted On

03 May, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Good communication skills

Industry

Education Management

Description

JOUW FUNCTIE

The Department of Mathematics of Vrije Universiteit Amsterdam welcomes applications for a two-year Postdoctoral position in Reinforcement Learning for Stochastic Optimization. The candidate is expected to conduct high-quality research at the intersection of reinforcement learning and stochastic optimization. The objective is to contribute to the development of novel methodologies that advance both theoretical understanding and practical application. The postdoc will actively explore how reinforcement learning techniques can be applied across a range of complex operational domains, such as energy systems, health care, and scheduling and planning. A strong interest in interdisciplinary collaboration and the ability to connect with ongoing departmental research efforts are essential. Preference will be given to candidates who can connect to existing research strengths in the department. We are an inclusive, interdisciplinary group, and diversity and internationalism are at the heart of our research principles, as well as our teaching practice.
The preferred starting date is 01.09.2025
Applications from all groups currently under-represented in academic posts are especially encouraged. We particularly welcome applications from women and people with an ethnic minority background.

Responsibilities
  • conducting research in the area of Reinforcement Learning (85%)
  • contributing to the teaching portfolio of the Department of Mathematics (15%)
  • conducting high-quality research at the intersection of reinforcement learning and stochastic optimization
  • developing novel methodologies that advance both theoretical insights and practical applications
  • collaborating across disciplines and contribute to ongoing research activities within the department
  • applying reinforcement learning techniques to a variety of operational domains, including energy systems, health care, and telecommunication systems
Loading...